class_docstr: Linear regression is an approach for modeling a scalar target :math:`y` as
  a linear function of one or more explanatory variables denoted :math:`X`.
  
  Given a set of features :math:`x_i`, and a label :math:`y_i`, linear
  regression interprets the probability that the label is in one class as
  a linear function of a linear combination of the features.
  
      .. math::
        f_i(\theta) =  \theta^T x + \epsilon_i
  
  where :math:`\epsilon_i` is noise.  An intercept term is added by appending
  a column of 1's to the features.  Regularization is often required to
  prevent overfitting by penalizing models with extreme parameter values. The
  linear regression module supports l1 and l2 regularization, which are added
  to the loss function.
  
  The composite objective being optimized for is the following:
  
      .. math::
         \min_{\theta} \sum_{i = 1}^{n} (\theta^Tx - y_i)^2 + \lambda_1 ||\theta||_1 + \lambda_2 ||\theta||^{2}_{2}
  
  where :math:`\lambda_1` is the ``l1_penalty`` and :math:`\lambda_2` is the
  ``l2_penalty``.
  
  This model cannot be constructed directly.  Instead, use
  :func:`turicreate.linear_regression.create` to create an instance
  of this model. A detailed list of parameter options and code samples
  are available in the documentation for the create function.
  
  Examples
  --------
  .. sourcecode:: python
  
      # Load the data (From an S3 bucket)
      >>> data =  turicreate.SFrame('https://static.turi.com/datasets/regression/houses.csv')
  
      # Make a linear regression model
      >>> model = turicreate.linear_regression.create(data, target='price', features=['bath', 'bedroom', 'size'])
  
      # Extract the coefficients
      >>> coefficients = model.coefficients
  
      # Make predictions
      >>> predictions = model.predict(data)
  
      # Evaluate the model
      >>> results = model.evaluate(data)
  
  See Also
  --------
  create
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docstr: Return a string description of the model, including a description of
  the training data, training statistics, and model hyper-parameters.
  
  Returns
  -------
  out : string
      A description of the model.
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code: def __str__(self):
      """
      Return a string description of the model, including a description of
      the training data, training statistics, and model hyper-parameters.
  
      Returns
      -------
      out : string
          A description of the model.
      """
  
      return self.__repr__()
  
file_path: turicreate/src/python/turicreate/toolkits/regression/linear_regression.py
imports: from __future__ import print_function
  from __future__ import division
  from __future__ import absolute_import
  import turicreate.toolkits._supervised_learning
  from turicreate.toolkits._supervised_learning import SupervisedLearningModel
  from turicreate.toolkits._internal_utils import _toolkit_repr_print, _toolkit_get_topk_bottomk, _summarize_coefficients, _raise_error_evaluation_metric_is_valid
label_desc: Use image node_id 3 for calling the LinearRegression obj's underlying member method code with example usage: obj.__str__() and returns: self